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Synapse
January 1, 2024Radiology81 citationsOpen Access

Present and Future Innovations in AI and Cardiac MRI

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MMManuel A. MoralesWMWarren J. ManningRNReza Nezafat

Key Points

  • To provide an overview of current and emerging applications of artificial intelligence and deep learning across all stages of cardiac MRI acquisition, reconstruction, and postprocessing.
  • Narrative review synthesizing artificial intelligence and deep learning methodologies applied to cardiac MRI workflows.
  • Evaluation of automated scanning protocols, image prescription algorithms, inline contouring, radiomics, and multimodal clinical data integration.
  • AI-driven automated scanners accelerate acquisition and enhance spatial and temporal resolution while eliminating the need for cardiac triggering or breath holding.
  • Inline deep learning pipelines automatically generate chamber contours, initial functional measurements, and advanced radiomic features directly during image reconstruction.
  • Integrating AI-extracted imaging markers with clinical, genetic, and wearable sensor data facilitates personalized cardiovascular risk stratification.

Abstract

Cardiac MRI is used to diagnose and treat patients with a multitude of cardiovascular diseases. Despite the growth of clinical cardiac MRI, complicated image prescriptions and long acquisition protocols limit the specialty and restrain its impact on the practice of medicine. Artificial intelligence (AI)-the ability to mimic human intelligence in learning and performing tasks-will impact nearly all aspects of MRI. Deep learning (DL) primarily uses an artificial neural network to learn a specific task from example data sets. Self-driving scanners are increasingly available, where AI automatically controls cardiac image prescriptions. These scanners offer faster image collection with higher spatial and temporal resolution, eliminating the need for cardiac triggering or breath holding. In the future, fully automated inline image analysis will most likely provide all contour drawings and initial measurements to the reader. Advanced analysis using radiomic or DL features may provide new insights and information not typically extracted in the current analysis workflow. AI may further help integrate these features with clinical, genetic, wearable-device, and "omics" data to improve patient outcomes. This article presents an overview of AI and its application in cardiac MRI, including in image acquisition, reconstruction, and processing, and opportunities for more personalized cardiovascular care through extraction of novel imaging markers.

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Cite This Study

Morales et al. (2024) studied this question.

synapsesocial.com/papers/695e66dee224eafb755b3d7ahttps://doi.org/10.1148/radiol.231269
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